Ryan J Hamby

Introduction There’s a version of this story where Kubernetes “came back.” That framing is wrong. Kubernetes never left—it lost the narrative. Between 2020 and 2023, the conversation was dominated by YAML-hell complaints, cluster complexity horror stories, and a genuine question: is this worth it for most teams? The backlash was real. But the adoption curve was not affected. Production usage kept climbing even as the discourse soured. Now, in 2026, the framing has shifted entirely. Kubernetes is the de facto substrate for running inference workloads at scale. The 2026 CNCF survey puts production adoption at 82%—not “emerging,” not “early majority.” It’s baseline infrastructure, like TCP/IP. You don’t get excited about TCP/IP. You also can’t run a distributed system without it. ...

The Problem You’ve optimized your multicast application. The code is clean. The algorithm is sound. Yet your latency numbers lag behind competitors, and you can’t figure out why. The bottleneck isn’t in your logic—it’s in the cable running under the floor. This isn’t flashy. No one writes blog posts about fiber optics at conferences. But here’s the reality: in high-frequency trading, real-time video delivery, and large-scale sensor networks, the choice between a generic fiber cable and an optimized one can mean the difference between 500ns and 5µs latency. That’s a 10x spread, and it starts at the physical layer. ...

Introduction Cache hierarchies are everywhere—from L1/L2/L3 on your CPU to Redis clusters backing your microservices. But the way we organize and manage these tiers has fundamentally transformed over the past three decades. What started as simple in-memory buffers has evolved into a sophisticated science of eviction policies, write-through strategies, and predictive prefetching. This post explores why caches are structured the way they are, how eviction strategies have improved, and what the actual data tells us about their effectiveness over time. ...